Instructions to use ArkhAngelLifeJiggy/apex-flash-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArkhAngelLifeJiggy/apex-flash-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ArkhAngelLifeJiggy/apex-flash-1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ArkhAngelLifeJiggy/apex-flash-1")
model = AutoModelForMultimodalLM.from_pretrained("ArkhAngelLifeJiggy/apex-flash-1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArkhAngelLifeJiggy/apex-flash-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArkhAngelLifeJiggy/apex-flash-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkhAngelLifeJiggy/apex-flash-1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ArkhAngelLifeJiggy/apex-flash-1

SGLang

How to use ArkhAngelLifeJiggy/apex-flash-1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ArkhAngelLifeJiggy/apex-flash-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkhAngelLifeJiggy/apex-flash-1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ArkhAngelLifeJiggy/apex-flash-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkhAngelLifeJiggy/apex-flash-1",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ArkhAngelLifeJiggy/apex-flash-1 with Docker Model Runner:
```
docker model run hf.co/ArkhAngelLifeJiggy/apex-flash-1
```

apex-flash-1

apex-flash-1 is Cantina Security's first open-weights security model, developed in partnership with Yeta (@yetalabs on X). It is a reinforcement learning post-train of GLM-5.3-Flash for focused investigations: reading code, using tools, pursuing an exploit, and verifying its effect in a running target.

Held-out case evaluation

We evaluated 60 tasks from 20 held-out vulnerability cases. Each case has guided whitebox, focused whitebox, and focused blackbox views. Targets ran in isolated environments, and verifiers checked the final target state. The table reports adjudicated first-draw pass@1.

Model	Tasks solved	Pass@1	Estimated cost for 60 tasks
Claude Opus 5 High	43/60	71.7%	$74.68 (provider pricing)
apex-flash-1	40/60	66.7%	$2.38
GLM-5.3-Flash	36/60	60.0%	$4.56 (provider pricing)

Capabilities

The checkpoint retains the base model's image-text-to-text architecture. Our reported evaluation covers text-based security tasks; we have not evaluated image or video performance.

Training and use

Training used production-like software and protocol environments with the Codex agent harness. The model is intended as a focused worker under a larger agent's direction. We recommend the Codex harness for this checkpoint.

What comes next

We are building harder, multi-step investigations as the model improves, broadening the data mix, and training across agent harnesses. We plan to publish more held-out and public benchmark results as they are validated. Explore Apex to see how this work supports production security.

Model tree for ArkhAngelLifeJiggy/apex-flash-1

Base model

zai-org/GLM-5.3-Flash

Finetuned

(25)

this model